Overview

Dataset statistics

Number of variables11
Number of observations16175430
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 GiB
Average record size in memory88.0 B

Variable types

Numeric11

Alerts

LongitudAcc is highly correlated with Fuel Rate and 2 other fieldsHigh correlation
EngineSpeed is highly correlated with EngineAirInletPressure and 2 other fieldsHigh correlation
Fuel Rate is highly correlated with Engine Load and 1 other fieldsHigh correlation
Engine Load is highly correlated with Boost Pressure and 2 other fieldsHigh correlation
Boost Pressure is highly correlated with Engine Load and 2 other fieldsHigh correlation
EngineAirInletPressure is highly correlated with EngineSpeed and 3 other fieldsHigh correlation
AcceleratorPedalPos is highly correlated with EngineSpeed and 4 other fieldsHigh correlation
VehicleSpeed is highly correlated with EngineSpeedHigh correlation
BrakePedalPos is highly correlated with AcceleratorPedalPosHigh correlation
Fuel Rate is highly skewed (γ1 = 45.05381217) Skewed
Timestamp has unique values Unique
LongitudAcc has 3749236 (23.2%) zeros Zeros
EngineSpeed has 297129 (1.8%) zeros Zeros
Fuel Rate has 3737573 (23.1%) zeros Zeros
Engine Load has 3754590 (23.2%) zeros Zeros
Boost Pressure has 758809 (4.7%) zeros Zeros
AcceleratorPedalPos has 6243511 (38.6%) zeros Zeros
VehicleSpeed has 2234261 (13.8%) zeros Zeros
BrakePedalPos has 13229388 (81.8%) zeros Zeros

Reproduction

Analysis started2022-11-23 15:37:28.295765
Analysis finished2022-11-23 15:56:20.660629
Duration18 minutes and 52.36 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Timestamp
Real number (ℝ≥0)

UNIQUE

Distinct16175430
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.084673856 × 1010
Minimum4.758328021 × 1010
Maximum1.117813444 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size123.4 MiB
2022-11-23T16:56:20.990569image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum4.758328021 × 1010
5-th percentile5.127381075 × 1010
Q16.267834658 × 1010
median8.243642692 × 1010
Q39.621343434 × 1010
95-th percentile1.080474353 × 1011
Maximum1.117813444 × 1011
Range6.419806414 × 1010
Interquartile range (IQR)3.353508776 × 1010

Descriptive statistics

Standard deviation1.837034532 × 1010
Coefficient of variation (CV)0.2272243215
Kurtosis-1.111404269
Mean8.084673856 × 1010
Median Absolute Deviation (MAD)1.411740533 × 1010
Skewness-0.1751080103
Sum1.30773076 × 1018
Variance3.374695871 × 1020
MonotonicityStrictly increasing
2022-11-23T16:56:21.191846image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.758328021 × 10101
 
< 0.1%
9.106960468 × 10101
 
< 0.1%
9.106960663 × 10101
 
< 0.1%
9.106960771 × 10101
 
< 0.1%
9.10696086 × 10101
 
< 0.1%
9.106960968 × 10101
 
< 0.1%
9.106961068 × 10101
 
< 0.1%
9.106961176 × 10101
 
< 0.1%
9.106961262 × 10101
 
< 0.1%
9.10696137 × 10101
 
< 0.1%
Other values (16175420)16175420
> 99.9%
ValueCountFrequency (%)
4.758328021 × 10101
< 0.1%
4.75832813 × 10101
< 0.1%
4.758328232 × 10101
< 0.1%
4.758328312 × 10101
< 0.1%
4.758328422 × 10101
< 0.1%
4.758328530 × 10101
< 0.1%
4.758328634 × 10101
< 0.1%
4.758328712 × 10101
< 0.1%
4.758328812 × 10101
< 0.1%
4.758328922 × 10101
< 0.1%
ValueCountFrequency (%)
1.117813444 × 10111
< 0.1%
1.117813437 × 10111
< 0.1%
1.117813424 × 10111
< 0.1%
1.117813413 × 10111
< 0.1%
1.117813406 × 10111
< 0.1%
1.117813394 × 10111
< 0.1%
1.117813384 × 10111
< 0.1%
1.117813373 × 10111
< 0.1%
1.117813366 × 10111
< 0.1%
1.117813354 × 10111
< 0.1%

WetTankAirPressure
Real number (ℝ≥0)

Distinct188
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.0795195
Minimum0
Maximum12.89365
Zeros30574
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size123.4 MiB
2022-11-23T16:56:21.450973image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.2046
Q110.82515
median11.1699
Q311.51465
95-th percentile11.8594
Maximum12.89365
Range12.89365
Interquartile range (IQR)0.6895

Descriptive statistics

Standard deviation0.9277010234
Coefficient of variation (CV)0.08373116031
Kurtosis59.62602948
Mean11.0795195
Median Absolute Deviation (MAD)0.34475
Skewness-6.287830511
Sum179215992.1
Variance0.8606291887
MonotonicityNot monotonic
2022-11-23T16:56:21.700680image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.032849388
 
5.3%
11.3078832510
 
5.1%
11.10095828945
 
5.1%
10.82515827575
 
5.1%
11.4457819910
 
5.1%
11.37675818766
 
5.1%
10.96305782144
 
4.8%
10.7562770063
 
4.8%
11.23885764861
 
4.7%
11.65255740124
 
4.6%
Other values (178)8141144
50.3%
ValueCountFrequency (%)
030574
0.2%
0.06895463
 
< 0.1%
0.1379424
 
< 0.1%
0.20685390
 
< 0.1%
0.2758260
 
< 0.1%
0.34475323
 
< 0.1%
0.4137317
 
< 0.1%
0.48265380
 
< 0.1%
0.5516310
 
< 0.1%
0.62055265
 
< 0.1%
ValueCountFrequency (%)
12.893651
 
< 0.1%
12.82472
 
< 0.1%
12.755752
 
< 0.1%
12.686812
 
< 0.1%
12.6178511
 
< 0.1%
12.548934
 
< 0.1%
12.4799596
 
< 0.1%
12.411179
 
< 0.1%
12.34205551
 
< 0.1%
12.27311561
< 0.1%

LongitudAcc
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct125
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.03213855211
Minimum-7.1
Maximum13
Zeros3749236
Zeros (%)23.2%
Negative6713262
Negative (%)41.5%
Memory size123.4 MiB
2022-11-23T16:56:21.945734image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-7.1
5-th percentile-1
Q1-0.2
median0
Q30.2
95-th percentile0.8
Maximum13
Range20.1
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.5870923095
Coefficient of variation (CV)-18.26754072
Kurtosis131.3431163
Mean-0.03213855211
Median Absolute Deviation (MAD)0.2
Skewness5.553683143
Sum-519854.9
Variance0.3446773799
MonotonicityNot monotonic
2022-11-23T16:56:22.138233image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03749236
23.2%
-0.11601736
9.9%
0.11365021
 
8.4%
-0.21360163
 
8.4%
0.21028054
 
6.4%
-0.3969350
 
6.0%
0.3771181
 
4.8%
-0.4656712
 
4.1%
0.4564522
 
3.5%
0.5453809
 
2.8%
Other values (115)3655646
22.6%
ValueCountFrequency (%)
-7.11
 
< 0.1%
-72
 
< 0.1%
-6.61
 
< 0.1%
-6.42
 
< 0.1%
-6.31
 
< 0.1%
-6.21
 
< 0.1%
-6.11
 
< 0.1%
-5.85
< 0.1%
-5.64
< 0.1%
-5.42
 
< 0.1%
ValueCountFrequency (%)
138703
0.1%
12.910
 
< 0.1%
6.71
 
< 0.1%
63
 
< 0.1%
5.72
 
< 0.1%
5.62
 
< 0.1%
5.56
 
< 0.1%
5.48
 
< 0.1%
5.39
 
< 0.1%
5.218
 
< 0.1%

EngineSpeed
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11881
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1076.56738
Minimum0
Maximum8191.875
Zeros297129
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size123.4 MiB
2022-11-23T16:56:22.333822image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile590
Q1906.125
median1163.75
Q31291.125
95-th percentile1463.875
Maximum8191.875
Range8191.875
Interquartile range (IQR)385

Descriptive statistics

Standard deviation322.2456133
Coefficient of variation (CV)0.2993269341
Kurtosis3.630577238
Mean1076.56738
Median Absolute Deviation (MAD)156.625
Skewness-0.785907382
Sum1.74139403 × 1010
Variance103842.2353
MonotonicityNot monotonic
2022-11-23T16:56:22.491724image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0297129
 
1.8%
600.2526175
 
0.2%
600.526109
 
0.2%
60025962
 
0.2%
599.7525870
 
0.2%
599.525641
 
0.2%
599.2525594
 
0.2%
600.87525414
 
0.2%
601.12525208
 
0.2%
601.37524669
 
0.2%
Other values (11871)15647659
96.7%
ValueCountFrequency (%)
0297129
1.8%
15.3751
 
< 0.1%
16.1252
 
< 0.1%
16.751
 
< 0.1%
17.1251
 
< 0.1%
17.251
 
< 0.1%
17.3752
 
< 0.1%
17.6256
 
< 0.1%
17.758
 
< 0.1%
17.8756
 
< 0.1%
ValueCountFrequency (%)
8191.875194
< 0.1%
2250.251
 
< 0.1%
2176.251
 
< 0.1%
2158.8751
 
< 0.1%
2149.6251
 
< 0.1%
2138.6251
 
< 0.1%
2131.251
 
< 0.1%
2129.8751
 
< 0.1%
2128.51
 
< 0.1%
2128.1251
 
< 0.1%

Fuel Rate
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1146
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.86278092
Minimum0
Maximum3876.198645
Zeros3737573
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size123.4 MiB
2022-11-23T16:56:22.668953image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.301234
median8.28058
Q322.239272
95-th percentile48.086511
Maximum3876.198645
Range3876.198645
Interquartile range (IQR)20.938038

Descriptive statistics

Standard deviation82.57428514
Coefficient of variation (CV)5.205536506
Kurtosis2101.817277
Mean15.86278092
Median Absolute Deviation (MAD)8.28058
Skewness45.05381217
Sum256587302.3
Variance6818.512566
MonotonicityNot monotonic
2022-11-23T16:56:22.858408image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03737573
 
23.1%
3.489673103654
 
0.6%
3.430526103116
 
0.6%
3.54882102085
 
0.6%
3.60796798549
 
0.6%
3.66711495051
 
0.6%
3.37137995051
 
0.6%
3.72626194689
 
0.6%
3.78540894479
 
0.6%
3.84455594012
 
0.6%
Other values (1136)11557171
71.4%
ValueCountFrequency (%)
03737573
23.1%
0.05914715315
 
0.1%
0.11829415058
 
0.1%
0.17744118192
 
0.1%
0.23658823390
 
0.1%
0.29573521374
 
0.1%
0.35488219379
 
0.1%
0.41402918144
 
0.1%
0.47317615722
 
0.1%
0.53232313350
 
0.1%
ValueCountFrequency (%)
3876.1986457121
< 0.1%
2773.2253891
 
< 0.1%
2707.8679541
 
< 0.1%
2670.3096091
 
< 0.1%
2660.6686481
 
< 0.1%
2619.8572181
 
< 0.1%
2551.1875511
 
< 0.1%
2551.0692571
 
< 0.1%
2525.3403121
 
< 0.1%
2523.211021
 
< 0.1%

Engine Load
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct201
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.14421947
Minimum0
Maximum100
Zeros3754590
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size123.4 MiB
2022-11-23T16:56:23.059413image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5
median25.5
Q346.5
95-th percentile92.5
Maximum100
Range100
Interquartile range (IQR)43

Descriptive statistics

Standard deviation28.21204881
Coefficient of variation (CV)0.9058518495
Kurtosis-0.1126133869
Mean31.14421947
Median Absolute Deviation (MAD)21.5
Skewness0.8287591997
Sum503771142
Variance795.9196981
MonotonicityNot monotonic
2022-11-23T16:56:23.274443image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03754590
 
23.2%
100539465
 
3.3%
20.5195811
 
1.2%
22.5195537
 
1.2%
22192780
 
1.2%
21.5192211
 
1.2%
21192132
 
1.2%
23190179
 
1.2%
20189390
 
1.2%
23.5183555
 
1.1%
Other values (191)10349780
64.0%
ValueCountFrequency (%)
03754590
23.2%
0.566062
 
0.4%
151293
 
0.3%
1.539864
 
0.2%
237357
 
0.2%
2.533718
 
0.2%
335533
 
0.2%
3.533649
 
0.2%
437809
 
0.2%
4.534726
 
0.2%
ValueCountFrequency (%)
100539465
3.3%
99.515646
 
0.1%
9916937
 
0.1%
98.519239
 
0.1%
9818438
 
0.1%
97.518209
 
0.1%
9718034
 
0.1%
96.517829
 
0.1%
9618112
 
0.1%
95.517112
 
0.1%

Boost Pressure
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct190
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2407906249
Minimum0
Maximum1.628802
Zeros758809
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size123.4 MiB
2022-11-23T16:56:23.469043image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.008618
Q10.060326
median0.12927
Q30.336102
95-th percentile0.844564
Maximum1.628802
Range1.628802
Interquartile range (IQR)0.275776

Descriptive statistics

Standard deviation0.2753647173
Coefficient of variation (CV)1.143585708
Kurtosis3.631207411
Mean0.2407906249
Median Absolute Deviation (MAD)0.112034
Skewness1.886434661
Sum3894891.897
Variance0.07582572755
MonotonicityNot monotonic
2022-11-23T16:56:23.638725image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.008618953943
 
5.9%
0.017236923879
 
5.7%
0758809
 
4.7%
0.094798630800
 
3.9%
0.103416605803
 
3.7%
0.08618582021
 
3.6%
0.112034533571
 
3.3%
0.077562468259
 
2.9%
0.120652442572
 
2.7%
0.025854409955
 
2.5%
Other values (180)9865818
61.0%
ValueCountFrequency (%)
0758809
4.7%
0.008618953943
5.9%
0.017236923879
5.7%
0.025854409955
2.5%
0.034472331147
 
2.0%
0.04309285919
 
1.8%
0.051708274049
 
1.7%
0.060326294336
 
1.8%
0.068944358841
 
2.2%
0.077562468259
2.9%
ValueCountFrequency (%)
1.6288024
 
< 0.1%
1.6201847
 
< 0.1%
1.6115669
 
< 0.1%
1.6029488
 
< 0.1%
1.594338
 
< 0.1%
1.58571210
 
< 0.1%
1.57709436
 
< 0.1%
1.56847648
 
< 0.1%
1.559858108
< 0.1%
1.55124131
< 0.1%

EngineAirInletPressure
Real number (ℝ≥0)

HIGH CORRELATION

Distinct95
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.1348283
Minimum50
Maximum510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size123.4 MiB
2022-11-23T16:56:23.830196image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile102
Q1106
median114
Q3134
95-th percentile186
Maximum510
Range460
Interquartile range (IQR)28

Descriptive statistics

Standard deviation27.57660281
Coefficient of variation (CV)0.22037512
Kurtosis4.068619548
Mean125.1348283
Median Absolute Deviation (MAD)10
Skewness1.906545805
Sum2024109656
Variance760.4690224
MonotonicityNot monotonic
2022-11-23T16:56:23.995188image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1021604595
 
9.9%
1101308048
 
8.1%
1121259388
 
7.8%
1041127369
 
7.0%
108900214
 
5.6%
114854384
 
5.3%
106712282
 
4.4%
116654743
 
4.0%
100520470
 
3.2%
118496468
 
3.1%
Other values (85)6737469
41.7%
ValueCountFrequency (%)
502
 
< 0.1%
521
 
< 0.1%
685
 
< 0.1%
821
 
< 0.1%
8426
 
< 0.1%
8621
 
< 0.1%
881
 
< 0.1%
94535
 
< 0.1%
968036
 
< 0.1%
9881245
0.5%
ValueCountFrequency (%)
510202
 
< 0.1%
5084
 
< 0.1%
26411
 
< 0.1%
26222
 
< 0.1%
26039
 
< 0.1%
258157
 
< 0.1%
256390
 
< 0.1%
254790
 
< 0.1%
2521565
< 0.1%
2502633
< 0.1%

AcceleratorPedalPos
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct251
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.37506754
Minimum0
Maximum100
Zeros6243511
Zeros (%)38.6%
Negative0
Negative (%)0.0%
Memory size123.4 MiB
2022-11-23T16:56:24.164348image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median42.4
Q368
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)68

Descriptive statistics

Standard deviation35.39829833
Coefficient of variation (CV)0.9224296032
Kurtosis-1.422346462
Mean38.37506754
Median Absolute Deviation (MAD)42.4
Skewness0.1983658573
Sum620733218.8
Variance1253.039524
MonotonicityNot monotonic
2022-11-23T16:56:24.330423image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06243511
38.6%
100927747
 
5.7%
64.878073
 
0.5%
65.677452
 
0.5%
64.476921
 
0.5%
62.476880
 
0.5%
66.476496
 
0.5%
61.276029
 
0.5%
67.275591
 
0.5%
60.875578
 
0.5%
Other values (241)8391152
51.9%
ValueCountFrequency (%)
06243511
38.6%
0.46397
 
< 0.1%
0.86752
 
< 0.1%
1.26583
 
< 0.1%
1.66866
 
< 0.1%
26457
 
< 0.1%
2.46722
 
< 0.1%
2.86948
 
< 0.1%
3.26586
 
< 0.1%
3.66996
 
< 0.1%
ValueCountFrequency (%)
100927747
5.7%
99.619014
 
0.1%
99.219674
 
0.1%
98.818787
 
0.1%
98.419574
 
0.1%
9819968
 
0.1%
97.620092
 
0.1%
97.220498
 
0.1%
96.819735
 
0.1%
96.420739
 
0.1%

VehicleSpeed
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1044
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.12837527
Minimum0
Maximum255.97971
Zeros2234261
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size123.4 MiB
2022-11-23T16:56:24.480863image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.498944
median38.794392
Q356.394828
95-th percentile75.592818
Maximum255.97971
Range255.97971
Interquartile range (IQR)39.895884

Descriptive statistics

Standard deviation24.6526816
Coefficient of variation (CV)0.6639849286
Kurtosis-0.4503887306
Mean37.12837527
Median Absolute Deviation (MAD)19.90107
Skewness0.06865688
Sum600567435.2
Variance607.7547103
MonotonicityNot monotonic
2022-11-23T16:56:24.630640image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02234261
 
13.8%
48.99686429100
 
0.2%
48.49689627740
 
0.2%
48.19613427726
 
0.2%
47.89537227399
 
0.2%
47.69616627291
 
0.2%
46.29391227006
 
0.2%
46.89543626987
 
0.2%
46.99699226987
 
0.2%
47.4969626803
 
0.2%
Other values (1034)13694130
84.7%
ValueCountFrequency (%)
02234261
13.8%
0.9999363303
 
< 0.1%
1.0975863967
 
< 0.1%
1.1991424689
 
< 0.1%
1.2967925434
 
< 0.1%
1.3983485577
 
< 0.1%
1.4999045889
 
< 0.1%
1.5975548135
 
0.1%
1.699116405
 
< 0.1%
1.796766765
 
< 0.1%
ValueCountFrequency (%)
255.97971194
 
< 0.1%
255.9758041479
< 0.1%
134.190631
 
< 0.1%
125.4919681
 
< 0.1%
105.2901361
 
< 0.1%
104.993281
 
< 0.1%
104.8917244
 
< 0.1%
104.7901682
 
< 0.1%
104.6925182
 
< 0.1%
104.5909621
 
< 0.1%

BrakePedalPos
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct240
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.874096874
Minimum0
Maximum96
Zeros13229388
Zeros (%)81.8%
Negative0
Negative (%)0.0%
Memory size123.4 MiB
2022-11-23T16:56:24.784373image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile19.2
Maximum96
Range96
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.789764373
Coefficient of variation (CV)2.362399275
Kurtosis6.413069056
Mean2.874096874
Median Absolute Deviation (MAD)0
Skewness2.43875188
Sum46489752.8
Variance46.10090024
MonotonicityNot monotonic
2022-11-23T16:56:24.958331image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013229388
81.8%
16166268
 
1.0%
14.4158445
 
1.0%
14.8137120
 
0.8%
15.6130755
 
0.8%
15.2125022
 
0.8%
14108403
 
0.7%
16.497442
 
0.6%
13.695242
 
0.6%
13.256159
 
0.3%
Other values (230)1871186
 
11.6%
ValueCountFrequency (%)
013229388
81.8%
0.448082
 
0.3%
0.831742
 
0.2%
1.219269
 
0.1%
1.614231
 
0.1%
214547
 
0.1%
2.413760
 
0.1%
2.814155
 
0.1%
3.215200
 
0.1%
3.612828
 
0.1%
ValueCountFrequency (%)
96155
< 0.1%
95.6196
< 0.1%
95.23
 
< 0.1%
94.84
 
< 0.1%
94.42
 
< 0.1%
9411
 
< 0.1%
93.68
 
< 0.1%
93.26
 
< 0.1%
92.82
 
< 0.1%
92.43
 
< 0.1%

Interactions

2022-11-23T16:55:09.977937image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:48:37.907097image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:49:17.681159image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:49:54.970552image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:50:36.563123image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:51:14.933405image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:51:51.076254image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:52:32.420788image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:53:11.675839image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:53:50.457479image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:54:31.660293image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:55:13.322551image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:48:41.799926image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:49:20.935376image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:49:58.633327image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:50:40.185136image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:51:18.169652image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:51:54.680622image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:52:36.052145image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:53:15.074412image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:53:54.217952image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:54:35.005296image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:55:16.616439image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:48:45.310008image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:49:24.321956image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:50:02.194730image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:50:43.766853image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:51:21.416356image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:51:58.222285image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:52:39.572489image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:53:18.476630image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:53:57.717290image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:54:38.385345image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:55:19.980154image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:48:48.829091image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T16:50:05.865131image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:50:47.363836image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:51:24.659926image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:52:03.016062image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:52:43.480901image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T16:48:52.631291image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:49:31.039591image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:50:09.421819image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:50:50.872094image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:51:27.813433image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:52:06.540651image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T16:53:25.689224image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T16:55:27.046280image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T16:50:54.451901image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T16:52:50.544564image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T16:54:09.128758image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:54:48.797902image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:55:30.373471image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:49:00.348496image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:49:37.860711image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T16:51:05.051644image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:51:41.000800image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T16:53:01.092544image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:53:39.815268image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:54:19.547799image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:54:59.192089image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:55:40.642688image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:49:10.953620image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:49:48.049653image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:50:28.067579image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:51:08.394391image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:51:44.257624image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:52:25.039232image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:53:04.711424image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:53:43.242358image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:54:23.075556image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:55:02.913399image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:55:43.887432image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:49:14.371831image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:49:51.411370image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:50:33.085296image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:51:11.722868image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:51:47.494154image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:52:28.776573image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:53:08.226388image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:53:46.881104image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:54:28.215539image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T16:55:06.481129image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2022-11-23T16:56:25.155099image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-23T16:56:25.436213image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-23T16:56:25.745609image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-23T16:56:26.051456image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-23T16:56:26.357603image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-23T16:55:44.434741image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-23T16:55:52.304018image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

TimestampWetTankAirPressureLongitudAccEngineSpeedFuel RateEngine LoadBoost PressureEngineAirInletPressureAcceleratorPedalPosVehicleSpeedBrakePedalPos
04.758328e+1011.032000.7582.6258.51716849.50.017236102.040.40.0000000.0
14.758328e+1011.032001.0664.50016.44286675.50.043090104.066.04.9996800.0
24.758328e+1011.032001.11123.87530.28326466.00.112034108.082.09.0970740.0
34.758328e+1011.032001.31656.50045.66148480.00.232686126.094.013.9991040.0
44.758328e+1011.03200-0.21767.37515.8513960.00.491226152.0100.016.5965940.0
54.758329e+1010.96305-0.3930.1259.81840220.00.456754150.0100.016.0966260.0
64.758329e+1010.963051.11055.00032.88573284.00.284394146.0100.018.5964660.0
74.758329e+1010.963051.01304.62544.18280990.00.491226174.0100.022.8969720.0
84.758329e+1010.963051.31507.37557.136002100.00.758384206.0100.027.0959220.0
94.758329e+1010.963050.21687.50033.71379045.51.060014176.0100.030.8964600.0

Last rows

TimestampWetTankAirPressureLongitudAccEngineSpeedFuel RateEngine LoadBoost PressureEngineAirInletPressureAcceleratorPedalPosVehicleSpeedBrakePedalPos
161754201.117813e+1111.79045-1.0661.3753.78540817.00.094798108.00.04.39815618.4
161754211.117813e+1111.790450.0613.6255.20493631.50.051708106.00.02.1990784.0
161754221.117813e+1111.72150-1.4599.6254.25858425.00.034472106.00.00.00000014.8
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